Instrument played by nodding

We believe that many people are interested in the field of music. Due in part to Corona's influence, there has been an increase in the number of people who are home-based. Therefore, the number of people who are interested in music has probably increased even more due to the idea of increasing the number of hobbies that can be done at home. However, we believe that it is difficult for people to actually play musical instruments, produce music, or otherwise engage in music on their own initiative. In addition, playing a musical instrument requires fine manipulation for people with disabilities, and some people are not able to play a musical instrument to begin with. Therefore, we began researching simple instruments that can be played with a nod by people who have given up playing musical instruments or who are unable to play due to hand disabilities. The purpose of this research was to lower the barrier to playing a musical instrument by making the operation of playing a musical instrument simpler. In the process of developing the instrument, we thought that it could also be used for stretching the neck, so we added a variety of stretching exercises, from nodding (forward bending) to back bending and side bending, to correct straight necks, which plague many people in modern society, while performing on the instrument. Exercises used for stretching were added to the playing method. As the first stage of the research, we used OpenCV's cascade classifier, an object detection feature, to detect faces and nod to enable simple playing of the instrument. In the second stage, we used FaceOSC to improve the accuracy of face recognition, making the research more interesting and usable for stretching. Finally, measurements were made on the range of motion for forward bending, back bending, and side bending, and experiments were conducted on the actual use of what was produced in the research. The range-of-motion data from the measurement side could not be used well, and problems arose, such as the fact that the recognition of faces was off, and if the face was covered with hair, the recognition would be off. In order to improve these issues, various improvement measures were taken, including reviewing the programming in the first place to take into account the range of motion of the face and neck, and reviewing the position of the camera.